Source code for the Paper: CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints}

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Deep Learning CombOptNet
Overview

CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints

Installation

  1. Run pipenv install (at your own risk with --skip-lock to save some time).
  2. From within the pipenv environment run python3 -m pip install -i https://pypi.gurobi.com gurobipy.
  3. Obtain a license and download/set it.
  4. Download and extract the datasets.

Usage

For [experiment] = knapsack or [experiment] = static_constraints:

  1. Set the base_dataset_path parameter in experiments/[experiment]/base.yaml.
  2. In case of static constraints: set the dataset_specification parameter in experiments/static_constraints/base.yaml
  3. Run python3 main.py experiments/[experiment]/[method].yaml.
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Comments
  • Graph matching code missing

    Graph matching code missing

    The training-code branch ReadMe says :

    For the results from the graph matching experiment please refer to the comboptnet branch in the BB-GM repository.

    There's no comboptnet branch in that repo. Has it been published anywhere else?

    opened by vdkhvb 0
Owner
Autonomous Learning Group
Autonomous Learning Group
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